1. A Dynamic Histogram Equalization for Image Contrast Enhancement
Source: IEEE Transactions on Consumer
Electronics, Vol. 53, No. 2, MAY 2007
Author: M. Abdullah-A-Wadud, Md. Hasanul
Kabir, M. Ali Akber Dewan, and
Oksam Chae
Speaker: Chih-Hao Chen
Date: 2008/04/30

2. Introduction
Original Image
Processed Image

3. Proposed Method(1/4) Input Image Output Image Phase1 Phase2
Histogram Partition
Gary Level Allocation
Histogram
Input Image
Output Image 1 2 3 4
255 1 2 3 4
255

4. Proposed Method(2/4) p1 p0 p2 One-dimensional smoothing filter:
p0: The processing pixel H1 1 2 3 4 H2

5. Proposed Method(3/4) u: mean value s: standard deviation
Marked area 1 2 3 1 2 3 4 5 6
u: mean value s: standard deviation
If marked area is less than 68.3% of current sub-histogram, splits again.

6. Proposed Method(4/4) Sectioni: Gray level range of sub-histogram i
Original image
Output image
Sectioni: Gray level range of sub-histogram i
Si: The summation of all histogram values of ith sub-
histogram
x: The coefficient to control the strength of image contrast

7. Experimental Results(1/3)
(a) Original image
(b)-(e) DHEed image (x = 0, 1,
2, 4, accordingly).

8. Experimental Results(2/3)
(a) Original image, (b) GHEed image, (c) DHSed image, (d) RMSHEed image (r = 2), (e) DHEed image (x = 0).

9. Experimental Results(3/3)
(a) Original image (b) GHEed image (c) DHSed image (d) RMSHEed image with r = 2 (e) DHEed image with x= 0.

10. Conclusions
DHE enhances the image contrast without making any loss in image details.
This method is simple and easy to implement.